YEARS

2009-2011

AUTHORS

Brett Mckinney

TITLE

Machine Learning Analysis of Genetic Modulators of Vaccine Immune Response

ABSTRACT

Machine Learning Analysis of Genetic Modulators of Vaccine Immune Response. This proposal describes the development of a machine-learning strategy to identify interacting susceptibility loci in polygenic biological endpoints, with a focus on smallpox and anthrax vaccine-related adverse events (AEs) and variation in serologic antibody response. The appearance of AEs following smallpox vaccination stems from excess stimulation of inflammatory pathways and is likely affected by multiple, interacting genetic factors. Some of these gene-gene interactions may be epistatic, having no distinct marginal effect for any single variant. Analytical approaches are needed for testing association in genome-wide data to account for conditional dependencies between genetic variants while still accounting for co-occurring variants with high marginal effects. We have introduced a machine-learning feature selection and optimization method called Evaporative Cooling (EC), which is based on information theory and the statistical thermodynamics of cooling a system of interacting particles by evaporation. The objective of the EC learner is the identification of susceptibility or protective genes in genome-wide DNA sequence data. This novel filter method, which includes no assumptions regarding gene interaction architecture or interaction order, has been shown to identify a spectrum of disease susceptibility models, including marginal main effects and pure interaction effects. Characterizing the genetic basis of multifactorial phenotypes in genome-wide sequence data is also computationally challenging due to the presence of a large number of noise variants, or variants that are irrelevant to the phenotype. Thus, the EC algorithm evaporates (i.e., removes) noise variants, leaving behind a minimal collection of variants enriched for relevance to the given phenotype. We propose to advance this method to characterize and interpret singe-gene, gene-gene and gene-environment interactions all of which may modulate complex phenotypes such as vaccine-associated AEs and human immune response. This strategy will be developed with the aid of artificial data, simulated under a variety of conditions observed in real data, and the strategy will be tested on single nucleotide polymorphism (SNP) and clinical data from volunteers from a NIAID/NIH-sponsored trial to evaluate the Aventis Pasteur Smallpox Vaccine and a Center for Disease Control sponsored trial to evaluate Anthrax Vaccine Adsorbed.

FUNDED PUBLICATIONS

  • Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine.
  • Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder
  • Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS.
  • Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder.
  • Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine
  • Encore: Genetic Association Interaction Network centrality pipeline and application to SLE exome data.
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    23 TRIPLES      17 PREDICATES      24 URIs      9 LITERALS

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    1 grants:04562f6cf33fc8f113fb5a393a737a51 sg:abstract Abstract Machine Learning Analysis of Genetic Modulators of Vaccine Immune Response. This proposal describes the development of a machine-learning strategy to identify interacting susceptibility loci in polygenic biological endpoints, with a focus on smallpox and anthrax vaccine-related adverse events (AEs) and variation in serologic antibody response. The appearance of AEs following smallpox vaccination stems from excess stimulation of inflammatory pathways and is likely affected by multiple, interacting genetic factors. Some of these gene-gene interactions may be epistatic, having no distinct marginal effect for any single variant. Analytical approaches are needed for testing association in genome-wide data to account for conditional dependencies between genetic variants while still accounting for co-occurring variants with high marginal effects. We have introduced a machine-learning feature selection and optimization method called Evaporative Cooling (EC), which is based on information theory and the statistical thermodynamics of cooling a system of interacting particles by evaporation. The objective of the EC learner is the identification of susceptibility or protective genes in genome-wide DNA sequence data. This novel filter method, which includes no assumptions regarding gene interaction architecture or interaction order, has been shown to identify a spectrum of disease susceptibility models, including marginal main effects and pure interaction effects. Characterizing the genetic basis of multifactorial phenotypes in genome-wide sequence data is also computationally challenging due to the presence of a large number of noise variants, or variants that are irrelevant to the phenotype. Thus, the EC algorithm evaporates (i.e., removes) noise variants, leaving behind a minimal collection of variants enriched for relevance to the given phenotype. We propose to advance this method to characterize and interpret singe-gene, gene-gene and gene-environment interactions all of which may modulate complex phenotypes such as vaccine-associated AEs and human immune response. This strategy will be developed with the aid of artificial data, simulated under a variety of conditions observed in real data, and the strategy will be tested on single nucleotide polymorphism (SNP) and clinical data from volunteers from a NIAID/NIH-sponsored trial to evaluate the Aventis Pasteur Smallpox Vaccine and a Center for Disease Control sponsored trial to evaluate Anthrax Vaccine Adsorbed.
    2 sg:endYear 2011
    3 sg:fundingAmount 346329.0
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    18 sg:scigraphId 04562f6cf33fc8f113fb5a393a737a51
    19 sg:startYear 2009
    20 sg:title Machine Learning Analysis of Genetic Modulators of Vaccine Immune Response
    21 sg:webpage http://projectreporter.nih.gov/project_info_description.cfm?aid=7919847
    22 rdf:type sg:Grant
    23 rdfs:label Grant: Machine Learning Analysis of Genetic Modulators of Vaccine Immune Response
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